A-DInSAR Performance for Updating Landslide Inventory in Mountain Areas: An Example from Lombardy Region (Italy)
Abstract
:1. Introduction
2. General Setting of the Study Areas
AOIs Description
3. Materials and Methods
4. Results
5. Discussion
6. Conclusions
- A-DInSAR analysis in single acquisition geometry, can work properly if the favorable geometry among descending or ascending is chosen with respect to the slope orientation the aspect of the study areas. The single geometry analysis can be successfully applied for the preliminary study of a large number of landslides, or a wide area of interest. About the 16% of information may however be lost, due to the surface irregularity and roughness of the natural slopes. The topographical complexity of the slopes makes the models of prediction of PSs coverage unreliable and the simple rule that ascending acquisition geometry is suitable for E-facing slopes, while descending mode is used for W-facing slopes is not always verified. For detailed studies, indeed, it is recommended to use both SAR acquisition geometries;
- A-DInSAR outputs can update the state of activity of the previously mapped landslides in about almost 70% of the analyzed areas of interest. In particular, these results provide new qualitative and quantitative data on the landslides, as well as precise information about the velocity of the process, and can redraw the landslide perimeters or detect not previously mapped landslides.
Author Contributions
Funding
Conflicts of Interest
References
- Varnes, D.J. Landslide Hazard Zonation: A Review of Principles and Practice; Natural Hazard Series; UNESCO: Paris, France, 1984; Volume 3. [Google Scholar]
- WP/WLI. Working Party on World Landslide Inventory Multilingual Glossary for Landslides; The Canadian Geotechnical Society. BiTech Publisher: Richmond, BC, Cannada, 1993. [Google Scholar]
- UN-ISDR. Terminology on Disaster Risk Reduction; United Nations, International Strategy for Disaster Reduction: Geneva, Switzerland, May 2019. Available online: http://www.unisdr.org/files/7817_UNISDRTerminologyEnglish.pdf (accessed on 8 May 2019).
- Corominas, J.; Einstein, H.; Davis, T.; Strom, A.; Zuccaro, G.; Nadim, F.; Verdel, T. Glossary of terms on landslide hazard and risk. In Engineering Geology for Society and Territory; Lollino, G., Giordan, D., Crosta, G.B., Corominas, J., Azzam, R., Wasowski, J., Sciarra, N., Eds.; Springer: Cham, Switzerland, 2015; Volume 2, pp. 1775–1779. [Google Scholar]
- Meisina, C.; Zucca, F.; Fossati, D.; Ceriani, M.; Allievi, J. Ground deformation monitoring by using the Permanent Scatterers Technique: The example of the Oltrepo Pavese (Lombardia, Italy). Eng. Geol. 2006, 88, 240–259. [Google Scholar] [CrossRef]
- Colesanti, C.; Wasowski, J. Investigating landslides with space-borne Synthetic Aperture Radar (SAR) interferometry. Eng. Geol. 2006, 88, 173–199. [Google Scholar] [CrossRef]
- Cascini, L.; Fornaro, G.; Peduto, D. Advanced low- and full-resolution DInSAR map generation for slow-moving landslide analysis at different scales. Eng. Geol. 2010, 112, 29–42. [Google Scholar] [CrossRef]
- Strozzi, T.; Delaloye, R.; Kääb, A.; Ambrosi, C.; Perruchoud, E.; Wegmuller, U. Combined observations of rock mass movements using satellite SAR interferometry, differential GPS, airborne digital photogrammetry, and airborne photography interpretation. J. Geophys. Res. Space Phys. 2010, 115. [Google Scholar] [CrossRef] [Green Version]
- Righini, G.; Pancioli, V.; Casagli, N. Updating landslide inventory maps using Persistent Scatterer Interferometry (PSI). Int. J. Remote. Sens. 2011, 33, 2068–2096. [Google Scholar] [CrossRef]
- Bozzano, F.; Rocca, A. Remote monitoring of deformation using Satellite SAR Interferometry. Geotech. News 2012, 30, 26. [Google Scholar]
- Cigna, F.; Del Ventisette, C.; Liguori, V.; Casagli, N. Advanced radar-interpretation of InSAR time series for mapping and characterization of geological processes. Nat. Hazards Earth Syst. Sci. 2011, 11, 865–881. [Google Scholar] [CrossRef]
- Herrera, G.; Gutierrez, F.; García-Davalillo, J.C.; Guerrero, J.; Notti, D.; Galve, J.P.; Fernandez-Merodo, J.A.; Cooksley, G. Multi-sensor advanced DInSAR monitoring of very slow landslides: The Tena Valley case study (Central Spanish Pyrenees). Remote Sens. Environ. 2013, 128, 31–43. [Google Scholar] [CrossRef]
- Jebur, M.N.; Pradhan, B.; Tehrany, M.S. Using ALOS PALSAR derived high-resolution DInSAR to detect slow-moving landslides in tropical forest: Cameron Highlands, Malaysia. Geomat. Nat. Hazards Risk 2015, 6, 741–759. [Google Scholar] [CrossRef]
- Notti, D.; Herrera, G.; Bianchini, S.; Meisina, C.; García-Davalillo, J.C.; Zucca, F. A methodology for improving landslide PSI data analysis. Int. J. Remote Sens. 2014, 35, 2186–2214. [Google Scholar]
- García-Davalillo, J.C.; Herrera, G.; Notti, D.; Strozzi, T.; Álvarez-Fernández, I. DInSAR analysis of ALOS PALSAR images for the assessment of very slow landslides: The Tena Valley case study. Landslides 2014, 11, 225–246. [Google Scholar] [CrossRef]
- Barra, A.; Monserrat, O.; Mazzanti, P.; Esposito, C.; Crosetto, M.; Mugnozza, G.S. First insights on the potential of Sentinel-1 for landslides detection. Geomat. Nat. Hazards Risk 2016, 7, 1874–1883. [Google Scholar] [CrossRef] [Green Version]
- Bozzano, F.; Mazzanti, P.; Perissin, D.; Rocca, A.; De Pari, P.; Discenza, M.E. Basin scale assessment of landslides geomorphological setting by advanced InSAR analysis. Remote Sens. 2017, 9, 267. [Google Scholar] [CrossRef]
- Moretto, S.; Bozzano, F.; Esposito, C.; Mazzanti, P.; Rocca, A. Assessment of landslide pre-failure monitoring and forecasting using satellite SAR interferometry. Geosciences 2017, 7, 36. [Google Scholar] [CrossRef]
- Zhang, Y.; Meng, X.; Jordan, C.; Novellino, A.; Dijkstra, T.; Chen, G. Investigating slow-moving landslides in the Zhouqu region of China using InSAR time series. Landslides 2018, 15, 1299–1315. [Google Scholar] [CrossRef]
- Bouali, E.H.; Oommen, T.; Escobar-Wolf, R. Mapping of slow landslides on the Palos Verdes Peninsula using the California landslide inventory and persistent scatterer interferometry. Landslides 2018, 15, 439–452. [Google Scholar] [CrossRef]
- Journault, J.; Macciotta, R.; Hendry, M.T.; Charbonneau, F.; Huntley, D.; Bobrowsky, P.T. Measuring displacements of the Thompson River valley landslides, south of Ashcroft, BC, Canada, using satellite InSAR. Landslides 2018, 15, 621–636. [Google Scholar] [CrossRef]
- Varnes, D.J. Slope movements, type and process. In Landslides Analysis and Control; Schuster, R.L., Krizel, R.J., Eds.; Transportation Research Board: Washinghton, DC, USA, 1978; pp. 11–33. [Google Scholar]
- Varnes, D.J. Landslide types and processes. Landslides Eng. Pract. 1958, 24, 20–47. [Google Scholar]
- Hungr, O. Some methods of landslide intensity mapping. In Landslide Risk Assessment, Proceedings of the International Workshop on Landslide Risk Assessment, Balkema, Rotterdam, 19–21 February 1997; Cruden, D., Fell, R., Eds.; Routledge: London, UK, 2018; pp. 215–226. [Google Scholar]
- Lateltin, O.; Haemmig, C.; Raetzo, H.; Bonnard, C. Landslide risk management in Switzerland. Landslides 2005, 2, 313–320. [Google Scholar] [CrossRef]
- Uzielli, M.; Nadim, F.; Lacasse, S.; Kaynia, A.M. A conceptual framework for quantitative estimation of physical vulnerability to landslides. Eng. Geol. 2008, 102, 251–256. [Google Scholar] [CrossRef]
- Ferretti, A.; Fumagalli, A.; Novali, F.; Prati, C.; Rocca, F.; Rucci, A. A new algorithm for processing interferometric data-stacks: SqueeSAR. IEEE Trans. Geosci. Remote Sens. 2011, 49, 3460–3470. [Google Scholar] [CrossRef]
- Crosetto, M.; Monteserrat, O.; Junger, A.; Crippa, B. Persistent scatterer interferometry: Potential and limits. In Proceedings of the ISPRS Workshop on High- Resolution Earth Imaging for Geospatial Information, Hannover, Germany, 2–5 June 2009. [Google Scholar]
- Perissin, D.; Wang, T. Repeat-Pass SAR interferometry with partially coherent targets. IEEE Trans. Geosci. Remote Sens. 2012, 50, 271–280. [Google Scholar] [CrossRef]
- Bianchini, S.; Cigna, F.; Righini, G.; Proietti, C.; Casagli, N. Landslide hotspot mapping by means of persistent scatterer interferometry. Environ. Earth Sci. 2012, 67, 1155–1172. [Google Scholar] [CrossRef]
- Cigna, F.; Bianchini, S.; Casagli, N. How to assess landslide activity and intensity with Persistent Scatterer Interferometry (PSI): The PSI-based matrix approach. Landslides 2013, 10, 267–283. [Google Scholar] [CrossRef]
- Raspini, F.; Bianchini, S.; Ciampalini, A.; Del Soldato, M.; Solari, L.; Novali, F.; Del Conte, S.; Rucci, A.; Ferretti, A.; Casagli, N. Continuous, semi-automatic monitoring of ground deformation using Sentinel-1 satellites. Sci. Rep. 2018, 8, 7253. [Google Scholar] [CrossRef] [PubMed]
- Farina, P.; Colombo, D.; Fumagalli, A.; Marks, F.; Moretti, S. Permanent Scatterers for landslide investigations: outcomes from the ESA-SLAM project. Eng. Geol. 2006, 88, 200–217. [Google Scholar] [CrossRef]
- Herrera, G.; Notti, D.; García-Davalillo, J.C.; Mora, O.; Cooksley, G.; Sánchez, M.; Arnaud, A.; Crosetto, M. Landslides analysis with C- and X-band satellite SAR data: The Portalet landslide area. Landslides 2011, 8, 195–206. [Google Scholar] [CrossRef]
- Notti, D.; Davalillo, J.C.; Herrera, G.; Mora, O. Assessment of the performance of X-band satellite radar data for landslide mapping and monitoring: Upper Tena Valley case study. Nat. Hazards Earth Syst. Sci. 2010, 10, 1865–1875. [Google Scholar] [CrossRef]
- Colombo, A.; Mallen, L.; Pispico, R.; Giannico, C.; Bianchi, M.; Savio, G. Mappatura regionale delle aree monitorabili mediante l’uso della tecnica PS. In Proceedings of the 10th National Conference ASITA, Bolzano, Italy, 14–17 November 2006. ISBN/ISSN:88-900943-0-3-2006. [Google Scholar]
- Dei Cas, L. Complementarietà fra i dati dell’interferometria satellitare e quelli ottenuti con strumentazione a terra sui versanti in dissesto. Rend. Online Soc. Geol. Ital. 2017, 42, 18–22. [Google Scholar] [CrossRef]
- Istituto Superiore per la Protezione e Ricerca Ambientale. Progetto IFFI-Inventario dei Fenomeni Franosi in Italia 2007. Available online: http://www.isprambiente.gov.it/it/progetti/suolo-e-territorio-1/iffi-inventario-dei-fenomeni-franosi-in-italia / (accessed on 8 May 2019).
- Trigila, A. Rapporto sulle Frane in Italia: Progetto IFFI metodologia, risultati e rapporti regionali; Agenzia per la protezione dell’ambiente e per i servizi tecnici, APAT, Rapporti 78; 2007; 681p. Available online: http://www.isprambiente.gov.it/files/pubblicazioni/rapporti/rapporto-frane-2007/4169_Rapporto_2007_78.pdf (accessed on 20 August 2019).
- Regione Lombardia. Frane Di Lombardia - Centri Abitati e Infrastrutture a Rischio Frana in Lombardia. Inventario Fenomeni Franosi Lombardia (IFFI). 2012. Available online: http://www.geoportale.regione.lombardia.it (accessed on 13 May 2019).
- Dal Piaz, G.V.; Bistacchi, A.; Massironi, M. Geological outline of the Alps. Episodes 2003, 26, 175–180. [Google Scholar] [Green Version]
- Berra, F.; Carminati, E. Subsidence history from backstripping analysis of the Permo-Mesozoic succession of the Central Southern Alps (Northern Italy). Basin Res. 2009, 22, 952–975. [Google Scholar] [CrossRef]
- Carelli, M.; Ceriani, M.; Valbuzzi, E. Analisi del dissesto da frana in Lombardia. In Rapporto sulle Frane in Italia: Progetto IFFI metodologia, risultati e rapporti regionali; Trigila, A., Ed.; APAT, 2007. Available online: http://www.isprambiente.gov.it/files/pubblicazioni/rapporti/rapporto-frane-2007/Capitolo_7_Lombardia.pdf (accessed on 20 August 2019).
- Braga, G.; Braschi, G.; Calculli, S.; Caucia, F.; Cerro, A.; Colleselli, F.; Grisolia, M.; Piccio, A.; Rossetti, R.; Setti, M.; et al. I fenomeni franosi nell'Oltrepo Pavese: tipologia e cause. Geol. Appl. Idrogeol. 1985, 20, 621–666. [Google Scholar]
- Handy, M.R.; Schmid, S.M.; Bousquet, R.; Kissling, E.; Bernoulli, D. Reconciling plate-tectonic reconstructions of Alpine Tethys with the geological–geophysical record of spreading and subduction in the Alps. Earth-Sci. Rev. 2010, 102, 121–158. [Google Scholar] [CrossRef]
- Crosetto, M.; Monserrat, O.; Cuevas-González, M.; Devanthéry, N.; Crippa, B. Persistent Scatterer Interferometry: A review. ISPRS J. Photogramm. Remote Sens. 2016, 115, 78–89. [Google Scholar] [CrossRef] [Green Version]
- Kampes, B.M. Radar Interferometry Persistent Scatterers Technique; Springer: Dordrecht, The Netherlands, 2006. [Google Scholar]
- Ferretti, A.; Prati, C.; Rocca, F. Permanent scatterers in SAR interferometry. IEEE Trans. Geosci. Remote. Sens. 2001, 39, 8–20. [Google Scholar] [CrossRef]
- Berardino, P.; Fornaro, G.; Lanari, R.; Sansosti, E. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Trans. Geosci. Remote. Sens. 2002, 40, 2375–2383. [Google Scholar] [CrossRef] [Green Version]
- Hooper, A.; Zebker, H.; Segall, P.; Kampes, B. A new method for measuring deformation on volcanoes and other natural terrains using InSAR persistent scatterers. Geophys. Res. Lett. 2004, 31, 23611. [Google Scholar]
- Meisina, C.; Zucca, F.; Notti, D.; Colombo, A.; Cucchi, A.; Savio, G.; Giannico, C.; Bianchi, M. Geological interpretation of PSInSAR data at regional scale. Sensors 2008, 8, 7469–7492. [Google Scholar] [CrossRef]
- Hanssen, R.F. Satellite radar interferometry for deformation monitoring: A priori assessment of feasibility and accuracy. Int. J. Appl. Earth Obs. Geoinformation 2005, 6, 253–260. [Google Scholar] [CrossRef]
- Italian National Geoportal Webpage, Italian Ministry of the Environment and for Protection of the Land and Sea. Available online: www.pcn.minambiente.it/mattm/en/not-ordinary-plan-of-remote-sensing. (accessed on 4 May 2019).
- Perissin, D. Interferometric SAR multitemporal processing: techniques and applications. In Multitemporal Remote Sensing; Springer: Cham, Switzerland, 2016; pp. 145–176. [Google Scholar]
- Lu, P.; Casagli, N.; Catani, F.; Tofani, V. Persistent scatterers interferometry hotspot and cluster analysis (psi-hca) for detection of extremely slow-moving landslides. Int. J. Remote Sens. 2012, 33, 466–489. [Google Scholar] [CrossRef]
Satellite | Envisat | COSMO-SkyMed |
---|---|---|
Sensor’s band | C-band | X-band |
Acquisition geometry | Asc. + Desc. | Asc. + Desc. |
Look angle (°) | 23 | 34 |
Time span | 2003–2009 | 2010–2014 |
No of images | - | 181 |
No of PSs for Asc. (sum of AOIs) | 417 | 6753 |
No of PSs for Desc. (sum of AOIs) | 873 | 5545 |
Mean PS density for Asc. (PS/km2) | 27.3 | 526.1 |
Mean PS density for Desc. (PS/km2) | 46.1 | 525.8 |
Velocity range for Asc. (max; min) (mm/yr) | −5.10; 0.24 | −10.77; 0.34 |
Velocity range for Desc. (max; min) (mm/yr) | −12.91; 0.61 | −7.35; 3.06 |
AOIS | ENVISAT (2003–2009) | COSMO-SkyMed (2010–2014) | ASPECT (°) | SLOPE (°) | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
ID | Name | N. of PSs (Desc.) | PSs/Km2 (Desc.) | N. of PSs (Asc.) | PSs/Km2 (Asc.) | N. of PSs (Desc.) | PSs/Km2 (Desc.) | N. of PSs (Asc.) | PSs/Km2 (Asc.) | |||
ID_1 | ACQUEBUONE | 90 | 33.43 | / | / | / | / | 848 | 314.96 | 0–22.5 | N–NNE | 18–25 |
ID_2 | POPOIA | 27 | 48.66 | 20 | 36.05 | / | / | 375 | 675.88 | 202.5–225 | SSW–SW | 18–25 |
ID_3 | VAL VEDETTA | 4 | 7.17 | / | / | / | / | 15 | 26.88 | 292–315.5 | WNW–NW | 25–32 |
ID_4 | GIUBEZZA | 4 | 5.20 | 49 | 62.02 | / | / | 938 | 1187.20 | 112.5–135 | ESE–SE | 11–18 |
ID_5 | PAISCO LOVENO | / | / | 25 | 34.60 | / | / | 505 | 698.97 | 157.5–180 | SSE–S | 18–25 |
ID_6 | GRUMELLO | / | / | 13 | 12.21 | / | / | 250 | 234.72 | 157.5–181 | SSE–S | 25–32 |
ID_7 | TROBIOLO | 12 | 19.33 | 1 | 1.61 | / | / | 420 | 676.49 | 292.5–315 | WNW–NW | 32–39 |
ID_8 | ZINVILL | / | / | 20 | 27.64 | / | / | 561 | 775.38 | 112.5–135 | ESE–SE | 32–39 |
ID_9 | DOSSENA | 78 | 136.62 | 31 | 54.30 | / | / | 945 | 1655.24 | 202.5–225 | SSW–SW | 18–25 |
ID_10 | PAGAFONE | 143 | 98.96 | 58 | 40.14 | / | / | 1481 | 1024.85 | 202.5–225 | SSW–SW | 11–18 |
ID_11 | CATASCO | 40 | 45.09 | 83 | 93.56 | 1362 | 1535.27 | / | / | 157.5–180 | SSE–S | 18–25 |
ID_12 | SAN NAZZARO | 78 | 157.12 | 16 | 32.23 | / | / | / | / | 202.5–225 | SSW–SW | 25–32 |
ID_13 | BEDOLESSO | / | / | / | / | / | / | / | / | 0–22.5 | N–NNE | 25–32 |
ID_14 | CORTENOVA | 28 | 21.56 | / | / | 987 | 760.10 | 77 | 59.30 | 225–247.5 | SW–WSW | 18–25 |
ID_15 | GARAVINA | 16 | 20.59 | 6 | 7.72 | 91 | 117.12 | 102 | 131.28 | 0–22.5 | N–NNE | 25–32 |
ID_16 | TORRIONI Di R. | 14 | 34.32 | / | / | 239 | 585.96 | / | / | 202.5–225 | SW–WSW | 46–53 |
ID_17 | RONCO | 3 | 17.86 | / | / | 24 | 222.88 | 5 | 16.43 | 0–22.5 | N–NNE | 18–25 |
ID_18 | SASSO DEL PEC. | 3 | 1.10 | 35 | 12.81 | 163 | 59.65 | 355 | 129.92 | 67.5–90 | ENE–E | 11–18 |
ID_19 | GERO BARCONE | 65 | 72.31 | 15 | 16.69 | 990 | 1101.33 | 644 | 716.42 | 202.5–225 | SSW–SW | 25–32 |
ID_20 | GANDELLINO | 38 | 27.38 | / | / | / | / | / | / | 247.5–270 | WSW–W | 25–32 |
ID_21 | SORIASCO | 25 | 77.48 | 13 | 40.29 | 228 | 706.59 | / | / | 202.5–225 | SSW–SW | 4–11 |
ID_22 | RUINELLO | 51 | 23.29 | 29 | 13.24 | 511 | 233.38 | / | / | 112.5–135 | ESE–SE | 4–11 |
ID_23 | VIGNOLA | 4 | 17.37 | / | / | 23 | 214.86 | / | / | 202.5–225 | SSW–SW | 18–25 |
ID_24 | POGGIO FER. | 11 | 25.21 | 1 | 2.80 | 116 | 324.96 | / | / | 270–292.5 | W–WNW | 11–18 |
ID_25 | RONCAGLIA | 29 | 18.17 | 2 | 1.25 | / | / | 102 | 63.91 | 292.5–315 | WNW–NW | 18–25 |
ID_26 | NOCENO | 22 | 34.71 | / | / | 284 | 448.11 | / | / | 225–247.5 | SW–WSW | 25–32 |
AOIs | Literature Information | Envisat (2003-2009) | COSMO-SkyMed (2010-2014) | Landslide Inventory Updating | State of Activity Updating | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ID | Name | Area (km2) | % Area Affected by Landslide | Sate of Activity (IFFI) | VLOS Desc. (mm/yr) | σ (VLOS Desc.) | VLOS Asc. (mm/yr) | σ (VLOS Asc.) | VLOS Desc. (mm/yr) | σ (VLOS Desc.) | VLOS Asc. (mm/yr) | σ (VLOS Asc.) | Velocity | Landslide Perimeter | Possible New landslides | ||
ID_1 | ACQUEBUONE | 2.70 | 100.0 | Un. | −3.04 | 4.08 | / | / | / | / | −10.77 | 0.59 | yes | yes | yes | yes | |
ID_2 | POPOIA | 0.55 | 14.4 | A | 0.61 | 0.42 | 0.13 | 0.40 | / | / | 0.34 | 0.62 | yes | no | no | yes | |
ID_3 | VAL VEDETTA | 0.70 | 27.1 | Un. | x | x | / | / | / | / | −1.17 | 0.98 | yes | no | no | no | |
ID_4 | GIUBEZZA | 0.79 | 39.2 | Un. | x | x | 0.02 | 0.46 | / | / | 0.23 | 1.02 | yes | no | no | yes | |
ID_5 | PAISCO LOVENO | 0.72 | 14.4 | A | / | / | −2.11 | 1.18 | / | / | −3.46 | 0.58 | yes | yes | no | yes | |
ID_6 | GRUMELLO | 1.10 | 29.1 | Un. | / | / | −0.55 | 0.43 | / | / | −0.17 | 1.58 | yes | yes | yes | yes | |
ID_7 | TROBIOLO | 0.62 | 38.7 | Q | −0.02 | 2.18 | x | x | / | / | −0.38 | 0.58 | yes | no | no | no | |
ID_8 | ZINVILL | 0.76 | 62.5 | Un. | / | / | −3.04 | 1.30 | / | / | −5.54 | 0.60 | yes | yes | yes | yes | |
ID_9 | DOSSENA | 0.24 | 42.1 | Un. | −2.11 | 2.00 | −0.48 | 0.66 | / | / | −1.90 | 0.47 | yes | no | no | yes | |
ID_10 | PAGAFONE | 1.40 | 100.0 | Un. | −4.07 | 1.42 | −0.77 | 0.75 | / | / | −2.44 | 0.47 | yes | no | yes | yes | |
ID_11 | CATASCO | 0.89 | 98.9 | Un. | −1.72 | 1.67 | −5.10 | 1.79 | −3.12 | 0.37 | / | / | yes | yes | yes | yes | |
ID_12 | SAN NAZZARO | 0.50 | 14.0 | Un. | −0.48 | 1.02 | −0.50 | 0.83 | / | / | / | / | yes | no | no | no | |
ID_13 | BEDOLESSO | 1.60 | 100.0 | Un. | / | / | / | / | / | / | / | / | no | no | no | no | |
ID_14 | CORTENOVA | 1.30 | 26.9 | Un. | −4.28 | 4.16 | / | / | −3.31 | 0.40 | −0.52 | 1.43 | yes | yes | yes | yes | |
ID_15 | GARAVINA | 0.78 | 100.0 | Un. | −5.59 | 1.74 | x | x | −6.18 | 0.43 | −4.99 | 0.47 | yes | yes | no | yes | |
ID_16 | TORRIONI Di R. | 0.41 | 75.6 | Un. | −0.13 | 0.59 | / | / | −2.74 | 0.42 | / | / | yes | yes | yes | yes | |
ID_17 | RONCO | 0.11 | 100.0 | Un. | x | x | / | / | −0.59 | 0.53 | x | x | yes | no | no | no | |
ID_18 | SASSO DEL PEC. | 2.70 | 28.5 | Un. | x | x | −0.32 | 0.60 | −0.25 | 0.54 | −0.72 | 0.46 | yes | no | no | yes | |
ID_19 | GERO BARCONE | 0.90 | 81.1 | Un. | 0.36 | 0.52 | −0.24 | 0.35 | −0.22 | 0.59 | 0.09 | 0.63 | yes | no | no | yes | |
ID_20 | GANDELLINO | 1.40 | 67.9 | Un. | −5.04 | 2.62 | / | / | / | / | / | / | yes | yes | no | no | |
ID_21 | SORIASCO | 0.32 | 20.6 | Q | −0.53 | 0.96 | −0.28 | 0.71 | −0.23 | 0.74 | / | / | yes | no | no | yes | |
ID_22 | RUINELLO | 2.20 | 63.6 | Q | −0.31 | 1.33 | −1.73 | 1.62 | 3.06 | 0.43 | / | / | yes | yes | yes | yes | |
ID_23 | VIGNOLA | 0.11 | 65.5 | Q | x | x | / | / | −0.72 | 0.75 | / | / | yes | no | no | no | |
ID_24 | POGGIO FER. | 0.36 | 50.0 | A | −3.68 | 4.12 | x | x | −7.35 | 0.46 | / | / | yes | yes | yes | yes | |
ID_25 | RONCAGLIA | 1.60 | 100.0 | Un. | −12.91 | 4.55 | x | x | / | / | -2.50 | 0.58 | yes | no | no | no | |
ID_26 | NOCENO | 0.63 | 15.9 | Un. | −0.41 | 1.62 | / | / | −2.77 | 0.40 | / | / | yes | yes | no | yes |
AOIs | Envisat (2003–2009) | Aspect (°) | Slope (°) | |||||
ID | Name | No PSs (desc) | PSs density (PS/km2) | N°PSs (asc) | PSs density (PS/km2) | |||
ID_2 | POPOIA | 27 | 48.66 | 20 | 36.05 | 202.5–225 | SSW-SW | 18–25 |
ID_4 | GIUBEZZA | 4 | 5.20 | 49 | 62.02 | 112.5–135 | ESE-SE | 11–18 |
ID_7 | TROBIOLO | 12 | 19.33 | 1 | 1.61 | 292.5–315 | WNW-NW | 32–39 |
ID_9 | DOSSENA | 78 | 136.62 | 31 | 54.30 | 202.5–225 | SSW-SW | 18–25 |
ID_10 | PAGAFONE | 143 | 98.96 | 58 | 40.14 | 202.5–225 | SSW-SW | 11–18 |
ID_11 | CATASCO | 40 | 45.09 | 83 | 93.56 | 157.5–180 | SSE-S | 18–25 |
ID_12 | SAN NAZZARO | 78 | 157.12 | 16 | 32.23 | 202.5–225 | SSW-SW | 25–32 |
ID_15 | GARAVINA | 16 | 20.59 | 6 | 7.72 | 0–22.5 | N-NNE | 25–32 |
ID_18 | SASSO DEL PEC. | 3 | 1.10 | 35 | 12.81 | 67.5–90 | ENE-E | 11–18 |
ID_19 | GERO BARCONE | 65 | 72.31 | 15 | 16.69 | 202.5–225 | SSW-SW | 25–32 |
ID_21 | SORIASCO | 25 | 77.48 | 13 | 40.29 | 202.5–225 | SSW-SW | 4–11 |
ID_22 | RUINELLO | 51 | 23.29 | 29 | 13.24 | 112.5-135 | ESE-SE | 4-11 |
ID_24 | POGGIO FER. | 9 | 25.21 | 1 | 2.80 | 270-292.5 | W-WNW | 11-18 |
ID_25 | RONCAGLIA | 29 | 18.17 | 2 | 1.25 | 292.5-315 | WNW-NW | 18-25 |
AOIs | COSMO-SkyMed (2010–2014) | Aspect (°) | Slope (°) | |||||
ID | Name | N° PSs (desc) | PSs density (PS/km2) | N°PSs (asc) | PSs density (PS/km2) | |||
ID_14 | CORTENOVA | 987 | 760.10 | 77 | 59.30 | 225–247.5 | SW-WSW | 18–25 |
ID_15 | GARAVINA | 91 | 117.12 | 102 | 131.28 | 0–22.5 | N-NNE | 25–32 |
ID_17 | RONCO | 24 | 222.88 | 5 | 46.43 | 0–22.5 | N-NNE | 18–25 |
ID_18 | SASSO DEL PEC. | 163 | 59.65 | 355 | 129.92 | 67.5–90 | ENE-E | 11–18 |
ID_19 | GERO BARCONE | 990 | 1101.33 | 644 | 716.42 | 202.5–225 | SSW-SW | 25–32 |
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Antonielli, B.; Mazzanti, P.; Rocca, A.; Bozzano, F.; Dei Cas, L. A-DInSAR Performance for Updating Landslide Inventory in Mountain Areas: An Example from Lombardy Region (Italy). Geosciences 2019, 9, 364. https://doi.org/10.3390/geosciences9090364
Antonielli B, Mazzanti P, Rocca A, Bozzano F, Dei Cas L. A-DInSAR Performance for Updating Landslide Inventory in Mountain Areas: An Example from Lombardy Region (Italy). Geosciences. 2019; 9(9):364. https://doi.org/10.3390/geosciences9090364
Chicago/Turabian StyleAntonielli, Benedetta, Paolo Mazzanti, Alfredo Rocca, Francesca Bozzano, and Luca Dei Cas. 2019. "A-DInSAR Performance for Updating Landslide Inventory in Mountain Areas: An Example from Lombardy Region (Italy)" Geosciences 9, no. 9: 364. https://doi.org/10.3390/geosciences9090364